🔅 Data Literacy: Exploring and Describing Data

🌐 Author: Barton Poulson
🔰 Level: Beginner

⏰ Duration: 5h 14m

🌀 Learn the fundamentals of data fluency and how data can help you make better decisions.

📗 Topics: Data Science

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📂 Full description

Data analysis isnt just for specialists who need to make sense of massive datasets. Decision-makers in every industry can benefit from a basic understanding of the goals and concepts of applied data analysis. In this course, Barton Poulson focuses on the fundamentals of data fluency, or the ability to work with data to extract insights and determine your next steps. Barton shows how exploring data with graphs and describing data with statistics can help you reach your goals and make better decisions. Instead of focusing on particular tools, he concentrates on general procedures that can help you solve specific problems. Barton covers how to prepare and adapt data, explore it visually, and use statistical methods to describe it. He goes in depth on probability and interference and also touches on data ethics and explainable AI.

📱Data Analysis
📱Data Literacy: Exploring and Describing Data

📖 10 Must know Data Analysis Concepts for beginners

🔅 Data Analytics with Observable

🌐 Author: Bill Shander
🔰 Level: Intermediate

⏰ Duration: 2h 9m

🌀 Get an overview of the Observable platform and build real skills fast.

📗 Topics: Data Visualization, Data Analytics

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📂 Full description

Observable is one of the most exciting (and constantly expanding) platforms to use for data analytics, visualization, storytelling, and collaboration. Its blend of easy-to-use click and drag features and customization makes it extremely popular for users across a wide range of skillsets. In this course, data analytics and visualization expert Bill Shander gives you an overview of the Observable platform, introducing the key features and functionalities with practical and tangible examples. Bill shows you how to get data into Observable, manipulate that data, and make visuals and reports with it. He demonstrates how to add interactivity to the reports, and how to make visuals in a variety of ways—some of which are one-click and others that offer more customizability with coding. By the end of the course, you will be able to spin up a new notebook, add data to it, and create a fully interactive data report.

📱Data Analysis
📱Data Analytics with Observable

📖 SQL Commands

🔰 PostgreSQL 101: The Everything Database

Built using C language, PostgreSQL is the most popular choice of database from small web apps to enterprise systems. It runs as a multi-process system and follows ACID principles.

📋 The key points about PostgreSQL’s Architecture are as follows:

1 - PostgreSQL supports concurrent client connections independently. Each client connection to PostgreSQL creates a dedicated server process.

2 - The Postmaster Process is the main supervisor that manages all other PostgreSQL processes. It controls the entire database instance.

3 - Background workers run parallel processes when needed to handle specialized tasks.

4 - PostgreSQL shared memory is a central memory area containing multiple buffers such as Shared, WAL, Clog, and Temporary buffers. All components communicate through this shared memory.

5 - PostgreSQL also has several auxiliary processes such as:
- BG Writer: Manages background writing
- WAL Writer: Handles write-ahead logging
- Auto Vacuum: Maintains database cleanliness
- Checkpointer: Ensures data consistency
- Stats Collector: Gathers statistics
- System Logger: Manages Logging
- Archiver: Handles archiving
- Replication launcher: Manages replication

6 - PostgreSQL has different types of physical files for varied needs such as:
- Data Files: Stores actual database data
- WAL Files: Write-ahead log storage
- Archive Files: Backup and recovery data
- Log Files: System and error logs